Artificial neural networks are composed essentially of neurons mutually interconnected by synapses, which are conventionally implemented by digital memories, but which can also be implemented by resistive components whose conductance varies as a function of the voltage applied across their terminals.
A learning rule conventionally used by spiking neural networks is the STDP (“Spike Timing Dependent Plasticity”) rule. This is a biologically inspired rule whose objective is to reproduce the operation of the learning carried out by biological neurons and synapses. In order for such a rule to be implemented, the conductance of the synapses must vary as a function of the relative instants of arrival of the pre and post synaptic pulses transmitted by the neurons connected respectively at input and at output. According to the STDP rule, the conductance of a synapse is increased if its post synaptic neuron is activated after its pre synaptic neuron and decreased in the converse case. Furthermore, the variation in conductance also depends on the precise lag between the pulses that are generated subsequent to the activation of the neurons. Typically the more significant the lag, the less the conductance will vary.
To implement an STDP rule or any other unsupervised learning method, it is necessary for the conductance of the memristive components which model the artificial synapses of the network to be able to vary in a progressive manner, as a function of the voltage applied across their terminals, both in the increasing and the decreasing direction. Furthermore, it is desirable that this can be done without prior knowledge of the state of conductance of the synapse.
Components of PCM type do not satisfy this property while moreover exhibiting characteristics suited to the practical production of neural networks, in particular high integration density, fast changes of state and decreased consumption.
Patent applications US 2010/0299296 and US 2010/0299297 are known, which describe an implementation of the STDP rule on the basis of unipolar devices of PCM type. These applications teach an implementation of an artificial neural network associated with the STDP learning rule with unipolar memristive devices of PCM type. However, because it seeks to imitate the biological STDP function, the proposed method requires the implementation of complex programming signals, which furthermore do not take account of the absence of progressive decrease in the conductance, in relative value, of such a device.